A Novel Framework of Reservoir Computing for Deterministic and Probabilistic Wind Power Forecasting

The path towards wind power forecasting has yielded huge socio–economic benefits at a global scale. However, most of the previous studies tend to emphasize the improvement of deterministic forecasting, usually losing sight of the significance of probabilistic forecasting. In this paper, a novel forecasting system that can perform deterministic and probabilistic forecasting of wind power simultaneously, composed by the modules of feature selection, forecasting, system optimization, and system evaluation is presented to further supplement the existing studies in this field. Concretely, a hybrid feature selection strategy is proposed in the feature selection module to determine optimal system input; superior to traditional gradient descent algorithm, a dynamic reservoir theory-based recurrent neural network is developed in the forecasting module; an enhanced multi-objective optimization algorithm with the objectives of accuracy and stability is proposed in the system optimization module to provide an optimal scenario for system parameters; the effectiveness and feasibility of the proposed system is then validated in the evaluation module. Moreover, the comprehensive performance analysis of the proposed system is investigated in depth. Finally, the experimental results demonstrate that the proposed system has a significant advantage over the benchmarks considered, further verifying its tremendous potential to be used in a practical wind power system.